Supporting decision making in design creativity through requirements identification and evaluation

References

• Albinet, A., Begoc, S., Boulanger, J. L., Casse, O., Dal, I., Dubois, H., Lakhal, F., Louar, D., Peraldi-Frati, M.A., Sorel, Y., & Van, Q. D. (2008, January). The MeMVaTEx methodology: From requirements to models in automotive application design. Embedded Real Time Software and Systems ERTS2008.
   Google Scholar
• Bauman, Y. I., Rudneva, Y. V., Mishakov, I. V., Plyusnin, P. E., Shubin, Y. V., Korneev, D. V., Stoyanovskii, V. O., Vedyagin, A. A., & Buyanov, R. A. (2019). Effect of Mo on the catalytic activity of Ni-based self-organizing catalysts for processing of dichloroethane into segmented carbon nanomaterials. Heliyon, 5(9), e02428. https://doi.org/10.1016/j.heliyon.2019.e02428
   PubMed Web of Science ®Google Scholar
• Bleuzé, T., Ciocci, M. C., Detand, J., & De Baets, P. (2014). Engineering meets creativity: A study on a creative tool to design new connections. International Journal of Design Creativity and Innovation, 2(4), 203–223. https://doi.org/10.1080/21650349.2014.892217
   Google Scholar
• Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012. https://doi.org/10.1016/j.techfore.2006.04.004
   Web of Science ®Google Scholar
• Farris, P. W., Bendle, N., Pfeifer, P., & Reibstein, D. (2010). Marketing metrics: The definitive guide to measuring marketing performance. Pearson Education.
   Google Scholar
• Flignor, P., & Orozco, D. (2006). Intangible Asset & Intellectual Property Valuation: A Multidisciplinary Perspective. World Intellectual Property Organization (WIPO), Genebra
   Google Scholar
• Han, J., Sarica, S., Shi, F., & Luo, J. (2021, September 9). Semantic networks for engineering design: State of the art and future directions. ASME Journal of Mechanical Design, 144(2), 020802. February 2022. https://doi.org/10.1115/1.4052148
   Web of Science ®Google Scholar
• Hassan, F. U., & Le, T. (2020). Automated requirements identification from construction contract documents using natural language processing. The Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12(2), 04520009. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000379
   Web of Science ®Google Scholar
• Jacoby, J. (1977). The emerging behavioral process technology in consumer decision-making research. ACR North American Advances.
   Google Scholar
• Jeon, J., Lee, C., & Park, Y. (2011). How to use patent information to search potential technology partners in open innovation. https://nopr.niscpr.res.in/handle/123456789/12688
   Google Scholar
• Lagrost, C., Martin, D., Dubois, C., & Quazzotti, S. (2010). Intellectual property valuation: How to approach the selection of an appropriate valuation method. Journal of Intellectual Capital, Emerald Group Publishing Limited, 11(4), 481–503. https://doi.org/10.1108/14691931011085641
   Google Scholar
• Liu, Q., Wang, K., Li, Y., & Liu, Y. (2020). Data-driven concept network for inspiring designers’ idea generation. Journal of Computing and Information Science in Engineering, 20(3). https://doi.org/10.1115/1.4046207
   PubMed Web of Science ®Google Scholar
• Livotov, P. (2015). Using patent information for identification of new product features with high market potential. Procedia Engineering, 131, 1157–1164. https://doi.org/10.1016/j.proeng.2015.12.438
   Google Scholar
• Mohammed, A. G., Ozgur, G., & Sevkat, E. (2019). Electrical resistance heating for deicing and snow melting applications: Experimental study. Cold Regions Science and Technology, 160, 128–138. https://doi.org/10.1016/j.coldregions.2019.02.004
   Web of Science ®Google Scholar
• Mohanani, R., Turhan, B., & Ralph, P. (2019). Requirements framing affects design creativity. IEEE Transactions on Software Engineering, 47(5), 936–947. https://doi.org/10.1109/TSE.2019.2909033
   Web of Science ®Google Scholar
• Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. (2007). Engineering design: A systematic approach (3rd ed.). Springer Science+ Business Media Deutschland GmbH.
   Google Scholar
• Russo, D., & Gervasoni, D. (2022, September). AI based patent analyzer for suggesting solutive actions and graphical triggers during problem solving. In Systematic Innovation Partnerships with Artificial Intelligence and Information Technology: 22nd International TRIZ Future Conference, TFC 2022, Warsaw, Poland, September 27–29, 2022, Proceedings (pp. 187–197). Cham: Springer International Publishing.
   Google Scholar
• Russo, D., Spreafico, M., & Precorvi, A. (2020). Discovering new business opportunities with dependent semantic parsers. Computers in Industry, 123, 103330. https://doi.org/10.1016/j.compind.2020.103330
   Web of Science ®Google Scholar
• Sarica, S., Luo, J., & Wood, K. L. (2020). TechNet: Technology semantic network based on patent data. Expert Systems with Applications, 142, 112995. https://doi.org/10.1016/j.eswa.2019.112995
   Web of Science ®Google Scholar
• Shi, F., Chen, L., Han, J., & Childs, P. (2017, October 2). A data-driven text mining and semantic network analysis for design information retrieval. ASME Journal of Mechanical Design, 139(11), 111402. November 2017. https://doi.org/10.1115/1.4037649
   Web of Science ®Google Scholar
• Ulwick, A. W. (2002). Turn customer input into innovation. Harvard Business Review, 80(1), 91–98.
   PubMed Web of Science ®Google Scholar
• Visvizi, A., Lytras, M. D., Damiani, E., & Mathkour, H. (2018). Policy making for smart cities: Innovation and social inclusive economic growth for sustainability. Journal of Science and Technology Policy Management, 9(2), 126–133. https://doi.org/10.1108/JSTPM-07-2018-079
   Web of Science ®Google Scholar
• Youe, W. J., Lee, S. M., Lee, S. S., Lee, S. H., & Kim, Y. S. (2016). Characterization of carbon nanofibre mats produced from electrospun lignin-g-polyacrylonitrile copolymer. International Journal of Biological Macromolecules, 82, 497–504. https://doi.org/10.1016/j.ijbiomac.2015.10.022
   PubMed Web of Science ®Google Scholar
• Zhao, L., Alhoshan, W., Ferrari, A., Letsholo, K. J., Ajagbe, M. A., Chioasca, E. V., & Batista-Navarro, R. T. (2021). Natural language processing for requirements engineering: A systematic mapping study. ACM Computing Surveys (CSUR), 54(3), 1–41. https://doi.org/10.1145/3444689
   Web of Science ®Google Scholar
• Zhu, Q., & Luo, J. (2022 November, 11). Generative transformers for design concept generation. ASME Journal of Computing and Information Science in Engineering, 1–61. https://doi.org/10.1115/1.4056220.
   Web of Science ®Google Scholar